Modern recordings of neural activity provide diverse observations of neurons across brain areas, behavioral conditions, and subjects -- thus presenting an exciting opportunity to reveal the fundamentals of brain-wide dynamics underlying cognitive function. Current methods, however, often fail to fully harness the richness of such data as they either provide an uninterpretable representation (e.g., via "black box" deep networks) or over-simplify the model (e.g., assume stationary dynamics or analyze each session independently). Here, instead of regarding asynchronous recordings that lack alignment in neural identity or brain areas as a limitation, we exploit these diverse views of the same brain system to learn a unified model of brain dynamics. We assume that brain observations stem from the joint activity of a set of functional neural ensembles (groups of co-active neurons) that are similar in functionality across recordings, and propose to discover the ensemble and their non-stationary dynamical interactions in a new model we term CrEIMBO (Cross-Ensemble Interactions in Multi-view Brain Observations). CrEIMBO identifies the composition of the per-session neural ensembles through graph-driven dictionary learning and models the ensemble dynamics as a latent sparse time-varying decomposition of global sub-circuits, thereby capturing non-stationary dynamics. CrEIMBO identifies multiple co-active sub-circuits while maintaining representation interpretability due to sharing sub-circuits across sessions. CrEIMBO distinguishes session-specific from global (session-invariant) computations by exploring when distinct sub-circuits are active. We demonstrate CrEIMBO's ability to recover ground truth components in synthetic data and uncover meaningful brain dynamics, capturing cross-subject and inter- and intra-area variability, in high-density electrode recordings of humans performing a memory task.

Machine learning is becoming an ever present part in our lives as many decisions, e.g. to lend a credit, are no longer made by humans but by machine learning algorithms. However those decisions are often unfair and discriminating individuals belonging to protected groups based on race or gender. With the recent General Data Protection Regulation (GDPR) coming into effect, new awareness has been raised for such issues and with computer scientists having such a large impact on peoples lives it is necessary that actions are taken to discover and prevent discrimination. This work aims to give an introduction into discrimination, legislative foundations to counter it and strategies to detect and prevent machine learning algorithms from showing such behavior.

A. Subsequently, a clustering algorithm such as k-means or the In practical scenarios for community detection, the cluster EM algorithm is applied to identify the clusters. The success of information is typically not accessible beforehand. The affinity this method depends on the quality of the data representation matrix A is often computed using a kernel function or a cosine and the accuracy of the computational methods used for A. similarity function, which may deviate from the ideal assignment A popular approach for cluster identification is to utilize matrix. Persisting in applying the spectral projection the top K eigenvectors of matrix A, as employed in spectral approximation to derive the optimal rank-K projection matrix clustering [1], [2]. Identifying these eigenvectors is equivalent, approximation can result in a matrix X with negative elements.

Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data, enabling the rapid adaptation to new unseen tasks. Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts. In this paper, we empirically and theoretically identify and analyze the model heterogeneity in DFML. We find that model heterogeneity introduces a heterogeneity-homogeneity trade-off, where homogeneous models reduce task conflicts but also increase the overfitting risk. Balancing this trade-off is crucial for learning shared representations across tasks. Based on our findings, we propose Task Groupings Regularization, a novel approach that benefits from model heterogeneity by grouping and aligning conflicting tasks. Specifically, we embed pre-trained models into a task space to compute dissimilarity, and group heterogeneous models together based on this measure. Then, we introduce implicit gradient regularization within each group to mitigate potential conflicts. By encouraging a gradient direction suitable for all tasks, the meta-model captures shared representations that generalize across tasks. Comprehensive experiments showcase the superiority of our approach in multiple benchmarks, effectively tackling the model heterogeneity in challenging multi-domain and multi-architecture scenarios.

The two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), consistently outperforms linear probing (LP) and FT alone in terms of accuracy for both in-distribution (ID) and out-of-distribution (OOD) data. This success is largely attributed to the preservation of pre-trained features, achieved through a near-optimal linear head obtained during LP. However, despite the widespread use of large language models, the exploration of complex architectures such as Transformers remains limited. In this paper, we analyze the training dynamics of LP-FT for classification models on the basis of the neural tangent kernel (NTK) theory. Our analysis decomposes the NTK matrix into two components, highlighting the importance of the linear head norm alongside the prediction accuracy at the start of the FT stage. We also observe a significant increase in the linear head norm during LP, stemming from training with the cross-entropy (CE) loss, which effectively minimizes feature changes. Furthermore, we find that this increased norm can adversely affect model calibration, a challenge that can be addressed by temperature scaling. Additionally, we extend our analysis with the NTK to the low-rank adaptation (LoRA) method and validate its effectiveness. Our experiments with a Transformer-based model on natural language processing tasks across multiple benchmarks confirm our theoretical analysis and demonstrate the effectiveness of LP-FT in fine-tuning language models. Code is available at https://github.com/tom4649/lp-ft_ntk.

This technical report presents the 1st winning model for UG2+, a task in CVPR 2024 UAV Tracking and Pose-Estimation Challenge. This challenge faces difficulties in drone detection, UAV-type classification and 2D/3D trajectory estimation in extreme weather conditions with multi-modal sensor information, including stereo vision, various Lidars, Radars, and audio arrays. Leveraging this information, we propose a multi-modal UAV detection, classification, and 3D tracking method for accurate UAV classification and tracking. A novel classification pipeline which incorporates sequence fusion, region of interest (ROI) cropping, and keyframe selection is proposed. Our system integrates cutting-edge classification techniques and sophisticated post-processing steps to boost accuracy and robustness. The designed pose estimation pipeline incorporates three modules: dynamic points analysis, a multi-object tracker, and trajectory completion techniques. Extensive experiments have validated the effectiveness and precision of our approach. In addition, we also propose a novel dataset pre-processing method and conduct a comprehensive ablation study for our design. We finally achieved the best performance in the classification and tracking of the MMUAD dataset. The code and configuration of our method are available at https://github.com/dtc111111/Multi-Modal-UAV.

Abstract: Face recognition is a very important topic in data science and biometric security research areas. It has multiple applications in military, finance, and retail, to name a few. In this paper, the novel hypergraph Laplacian Eigenmaps will be proposed and combine with the k nearest-neighbor method and/or with the kernel ridge regression method to solve the face recognition problem. Experimental results illustrate that the accuracy of the combination of the novel hypergraph Laplacian Eigenmaps and one specific classification system is similar to the accuracy of the combination of the old symmetric normalized hypergraph Laplacian Eigenmaps method and one specific classification system. Keywords: face recognition, hypergraph, Laplacian Eigenmaps, classification I. Introduction Given a relational dataset, the pairwise relationships among objects/entities/samples in this dataset can be represented as the weighted graph.

Despite the advancements in learning governing differential equations from observations of dynamical systems, data-driven methods are often unaware of fundamental physical laws, such as frame invariance. As a result, these algorithms may search an unnecessarily large space and discover equations that are less accurate or overly complex. In this paper, we propose to leverage symmetry in automated equation discovery to compress the equation search space and improve the accuracy and simplicity of the learned equations. Specifically, we derive equivariance constraints from the time-independent symmetries of ODEs. Depending on the types of symmetries, we develop a pipeline for incorporating symmetry constraints into various equation discovery algorithms, including sparse regression and genetic programming. In experiments across a diverse range of dynamical systems, our approach demonstrates better robustness against noise and recovers governing equations with significantly higher probability than baselines without symmetry.

This paper presents a framework that selectively triggers security reviews for incoming source code changes. Functioning as a review bot within a code review service, the framework can automatically request additional security reviews at pre-submit time before the code changes are submitted to a source code repository. Because performing such secure code reviews add cost, the framework employs a classifier trained to identify code changes with a high likelihood of vulnerabilities. The online classifier leverages various types of input features to analyze the review patterns, track the software engineering process, and mine specific text patterns within given code changes. The classifier and its features are meticulously chosen and optimized using data from the submitted code changes and reported vulnerabilities in Android Open Source Project (AOSP). The evaluation results demonstrate that our Vulnerability Prevention (VP) framework identifies approximately 80% of the vulnerability-inducing code changes in the dataset with a precision ratio of around 98% and a false positive rate of around 1.7%. We discuss the implications of deploying the VP framework in multi-project settings and future directions for Android security research. This paper explores and validates our approach to code change-granularity vulnerability prediction, offering a preventive technique for software security by preemptively detecting vulnerable code changes before submission.

We study nonconvex zeroth-order optimization (ZOO) in a high-dimensional space $\mathbb R^d$ for functions with approximately $s$-sparse gradients. To reduce the dependence on the dimensionality $d$ in the query complexity, high-dimensional ZOO methods seek to leverage gradient sparsity to design gradient estimators. The previous best method needs $O\big(s\log\frac ds\big)$ queries per step to achieve $O\big(\frac1T\big)$ rate of convergence w.r.t. the number T of steps. In this paper, we propose *Gradient Compressed Sensing* (GraCe), a query-efficient and accurate estimator for sparse gradients that uses only $O\big(s\log\log\frac ds\big)$ queries per step and still achieves $O\big(\frac1T\big)$ rate of convergence. To our best knowledge, we are the first to achieve a *double-logarithmic* dependence on $d$ in the query complexity under weaker assumptions. Our proposed GraCe generalizes the Indyk--Price--Woodruff (IPW) algorithm in compressed sensing from linear measurements to nonlinear functions. Furthermore, since the IPW algorithm is purely theoretical due to its impractically large constant, we improve the IPW algorithm via our *dependent random partition* technique together with our corresponding novel analysis and successfully reduce the constant by a factor of nearly 4300. Our GraCe is not only theoretically query-efficient but also achieves strong empirical performance. We benchmark our GraCe against 12 existing ZOO methods with 10000-dimensional functions and demonstrate that GraCe significantly outperforms existing methods.